1 Welcome!

Some information about these html documents. They are called notebooks, and they will provide the backbone of the workshop. They include explanatory text such as this, and R code and R code output. This combination allows you to see the code and the output and also have a detailed explanation for each section that you can come back to whenever needed.

At the start of each new section you’ll see a list of the main packages used for the following parts, the main functions covered and also some additional materials, resources that you’ll find helpful.

1.1 Why R?

  • One of the most used statistical tools in academia and in industry
  • It is open source, which means that the functions are not black boxes; when in doubt you can check what happens under the hood.
  • Because it is open source, it is at the cutting-edge of implementing new methods.
  • The R developer and user community is great in numbers and in resources. If you have a problem, chances are there is a package for that or an online discussion on how to solve it.
  • Reproducible research, which means that your code will produce the exact same output, making it easier to replicate your results or verify your (or others’) methods.
  • Statistical programming is a great transferable skill to have
  • Free of charge

1.2 Goal of the workshop

  • Give an overview of the state of the art data visualization practices in R
  • Provide an accessible way into ggplot2
  • Give practical examples

1.3 Workshop outline

Session 1

  • A very-very-very brief intro to data visualization principles
  • A gentle intro to ggplot2
    • How to make ggplot plots from the ground up
    • Exploratory data analysis using visualizations
    • How to create the most used plot types
  • Themes, modifying the plot elements

Session 2

  • Visualizing hypothesis testing
    • t-test, anova, regressions
  • Some other useful plot types
    • maps, networks, etc.

Session 3

  • Interactive plots
  • Animated plots
  • Extending ggplot2
  • Creating a new ggplot2 theme

Session 4

  • Using RMarkdown
  • Creating tables and exporting them

2 Data visualization principles1

The principle of proportional ink: The sizes of shaded areas in a visualization need to be proportional to the data values they represent.

  • Linear scale should always start at 0.
  • Color needs to be applied to serve a purpose, it must be clear, and it must not distract.
  • Consider accessibility, keep your audience in mind.
  • Consider redundant coding: it prompts us to encode data redundantly, using multiple different aesthetic dimensions.
  • For large datasets, consider applying small multiples.
  • Label chart elements properly and informatively
  • Pay attention to your axis labels, axis tick labels, and other assorted plot annotations.
  • Avoid line drawings
  • ABSOLUTELY NO 3D (PIE) CHARTS.

source: Darkhorse Analytics

Supplementary resources:

First things first, let’s load our packages.

# loading and shaping data
library(readr)
library(dplyr)
library(haven)

# data sources
library(gapminder)
library(palmerpenguins)

# general data visualisation
library(ggplot2)
library(ggridges)
library(ggthemes)
library(gghighlight)
library(ggforce)

Let’s also load our data that we’ll be using this session. (don’t worry about the message about the duplicated column names for now)

# data
penguins_df <- palmerpenguins::penguins

gapminder_df <- gapminder

ess_hun <- read_csv("data/ESS_Hun_7.csv")

oecd_sum <- read_csv("data/oecd_sum.csv")

stocks <- read_csv("data/stocks.csv")

The name of ggplot stands for grammar of graphics and it enables you to build your plot layer by layer and having the ability to control every detail of the output (if you so wish). It is used by many in academia, data journalism and the data analysis / data science industry2. During this workshop we will go through various types of data visualizations and try to apply the above set of principles to our output.

You create plots with the below syntax:

This can be a little bit overwhelming but worry not as when this workshop ends you’ll be familiar with each element in this general description of a ggplot2 plot.

2.1 The logic of a ggplot2 plot

To kickstart this session, let’s review how a plot is made and extend our knowledge on how to fine tune elements of the plot. This section was inspired by the great RLadies presentation of Eva Maerey We will use the Palmerson Penguins dataset, that contains data for 344 penguins. There are 3 different species of penguins in this dataset, collected from 3 islands in the Palmer Archipelago, Antarctica. The original publication of this data is in Gorman, Williams & Fraser (2014).

Artwork by allison_horst

Now let’s build a ggplot2 plot from the grounds up!

1

First, we specify the data we want to use within our ggplot() function call with the data = argument.

ggplot(data = penguins_df)

2

Second, we decide on the dimensions of our data. Let’s start by specifying what to plot on the y and x axes. This is done within the aes() argument, which stands for ‘aesthetic’.

ggplot(data = penguins_df,
       mapping = aes(x = body_mass_g,
                     y = flipper_length_mm))

3

Third, we add our wanted representation of the data, with the geom_ function family.

ggplot(data = penguins_df,
       mapping = aes(x = body_mass_g,
                     y = flipper_length_mm)) +
  geom_point()
#> Warning: Removed 2 rows containing missing values (geom_point).

4

Fourth, we can add further dimension to our plot by extending the aes() arguments. Let’s add colors based on the species variable.

ggplot(data = penguins_df,
       mapping = aes(x = body_mass_g,
                     y = flipper_length_mm,
                     color = species)) +
  geom_point()
#> Warning: Removed 2 rows containing missing values (geom_point).

5

Fifth, each aesthetic can be rescaled. Now we want to rescale our colors. We will use the manual color scale to specify each value. Colors can be added as HEX code, or names.

ggplot(data = penguins_df,
       mapping = aes(x = body_mass_g,
                     y = flipper_length_mm,
                     color = species)) +
  geom_point() +
  scale_color_manual(values = c("#7fc97f", "blue", "#fdc086"))
#> Warning: Removed 2 rows containing missing values (geom_point).

6

Sixth, we can modify the textual elements of our plot. To do this, we can assign a string to every text element with the labs function. As we see the color aesthetic created automatically a legend on the side. We can remove the title of it should we want it.

ggplot(data = penguins_df,
       mapping = aes(x = body_mass_g,
                     y = flipper_length_mm,
                     color = species)) +
  geom_point() +
  scale_color_manual(values = c("#7fc97f", "blue", "#fdc086")) +
  labs(
    title = "Penguins, their mass and their flippers",
    subtitle = "The positive relationship between body mass and flipper size",
    caption = "Data: Gorman, Williams & Fraser (2014)",
    x = "Body mass (g)",
    y = "Flipper length (mm)",
    color = ""
    )
#> Warning: Removed 2 rows containing missing values (geom_point).

7

Finally, we decide on the theme of our hearts. ggplot2 offers an ocean of customization options for our plot, there are some pre-made themes but we can create our own as well. Now we will stick to theme_minimal().

ggplot(data = penguins_df,
       mapping = aes(x = body_mass_g,
                     y = flipper_length_mm,
                     color = species)) +
  geom_point() +
  scale_color_manual(values = c("#7fc97f", "blue", "#fdc086")) +
 labs(
   title = "Penguins, their mass and their flippers",
   subtitle = "The positive relationship between body mass and flipper size",
   caption = "Data: Gorman, Williams & Fraser (2014)",
   x = "Body mass (g)",
   y = "Flipper length (mm)",
   color = ""
   ) +
  theme_minimal()
#> Warning: Removed 2 rows containing missing values (geom_point).

2.2 Data visualization staples

In this section we will cover various key types of visualizations. The structure of this section follows the structure of Claus Wilke’s great Fundamentals of Data Visualization book. Here we will cover the most often used type of plots, how to make them and how to address/avoid some of the typical issues.

2.2.1 Amounts and proportions

Source: Fundamentals of Data Visualization, 5.1 ch

Bars

We can use the bar charts to visualize categorical data. Let’s prep some data. We convert the character variables to factors, because this will let the ggplot function know the ordering of our categorical variables. If we want to order our variables according to a particular order, re-leveling the factors are the solution for that.

ess_hun <- ess_hun %>% 
  mutate(
    gndr = factor(gndr, labels = c("Female", "Male")),
    polintr = factor(polintr, labels = c("Very interested", "Quite interested", "Hardly interested", "Not at all interested", "Refusal", "Don't know"), ordered  = TRUE)
  )

There are two ways to create a barcharts with ggplot2: geom_bar() and geom_col(). The difference between these two is well described by the ggplot2 documentation: “geom_bar() makes the height of the bar proportional to the number of cases in each group (or if the weight aesthetic is supplied, the sum of the weights). If you want the heights of the bars to represent values in the data, use geom_col() instead. geom_bar() uses stat_count() by default: it counts the number of cases at each x position. geom_col() uses stat_identity(): it leaves the data as is.”

We will be using geom_bar() for this section. Let’s see the political interest of the Hungarian people.

ggplot(ess_hun, aes(polintr)) +
  geom_bar()

We can use the fill option to map another variable onto our plot. Let’s see how these categories are further divided by the gender of the respondents. By default we get a stacked bar chart.

ggplot(ess_hun, aes(polintr, fill = gndr)) +
  geom_bar()

we can use the position function in the geom_bar to change this. Another neat trick to make our graph more readable is coord_flip.

ggplot(ess_hun, aes(polintr, fill = gndr)) +
  geom_bar(position = "dodge") +
  coord_flip()

Let’s make sure that the bars are proportional. For this we can use the y = ..prop.. and group = 1 arguments, so the y axis will be calculated as proportions. The ..prop.. is a temporary variable that has the .. surrounding it so there is no collision with a variable named prop.

ggplot(ess_hun, aes(polintr, fill = gndr)) +
  geom_bar(position = "dodge", aes(y = ..prop.., group = gndr)) +
  coord_flip()

Combining categorical data and continuous data and using group by is also doable. We just create a grouped data and have the needed variables computed, then plot it. For this example, we’ll compute the average bill length for each penguin species in our sample and plot it with a barplot.

penguins_sum <- penguins_df %>% 
    group_by(species) %>% 
    summarise(avg_bill_length = mean(bill_length_mm, na.rm = TRUE))

Artwork by allison_horst

ggplot(penguins_sum, aes(species, avg_bill_length)) +
  geom_bar(stat = "identity") +
  coord_flip()

Dots

A special type of dot plots is the lollipop chart. The lollipop chart is a better barchart in a sense that it conveys the same information with better data/ink ratio. It also looks better. (note: some still consider it a gimmick)

For this we will modify a chart from the Data Visualisation textbook

This chart is built in a more complex way as we have to draw the lines and the dots separately. We draw the lines with the geom_segment that requires a starting value and ending value for both the x and y axis. The dots are drawn with the geom_point and the colors are from a dummy variable in the dataset.

# for the data see the github repository of the workshop

ggplot(oecd_sum, aes(x = year, y = diff, color = hi_lo)) +
  geom_segment(aes(y = 0, x = year, yend = diff, xend = year)) +
  geom_point() +
  theme(legend.position="none") +
  labs(
    x = NULL, y = "Difference in Years",
    title = "The US Life Expectancy Gap",
    subtitle = "Difference between US and OECD average life expectancies, 1960-2015",
    caption = "Adapted from Kieran Healy's Data Visualisation (2019), fig.4.21 "
    )

Heatmaps

The idea behind heatmaps is to map the values onto colors instead of bars or dots. For this we will use the geom_tile function. To have a manageable size we’ll subset the gapminder dataframe for European countries and pipe that data right into the ggplot function. It is always good practice to have some logic guiding the order of the categorical variables apart from alphabetical order. For this plot we use the reorder() function to reorder the countries by their gdp.

gapminder_df %>% 
  filter(continent == "Europe") %>% 
  ggplot(aes(year, reorder(country, gdpPercap), fill = gdpPercap)) +
  geom_tile(color = "grey")

2.3 Distributions

Source: Fundamentals of Data Visualization, 5.1 ch

Histogram

Now let’s introduce ourselves to the gapminder dataset and check some of the variables’ distribution. The data is a panel dataset, so our observations are country-years.

ggplot(gapminder_df, aes(x = lifeExp)) +
  geom_histogram()

To add some flair to our figure, we use color and fill inside the geom_ call. What is the difference between the two? Note that here we changed the bindiwdth parameter of the geom_histogram and the resolution of the histogram changed accordingly.

ggplot(gapminder_df, aes(x = lifeExp)) +
  geom_histogram(binwidth = 1, color = "black", fill = "orange") # we can set the colors and border of the bars and set the binwidth or bins

We can plot the distribution by continent as well, using the fill aesthetic option.

ggplot(gapminder_df, aes(x = lifeExp, fill = continent)) +
  geom_histogram()

Density plot

A variation on histograms is called density plots that uses Kernel smoothing (fancy! but in reality is a smoothing function which uses the weighted averages of neighboring data points.)

ggplot(penguins_df, aes(x = body_mass_g)) +
  geom_density()
#> Warning: Removed 2 rows containing non-finite values (stat_density).

Add some fill

ggplot(penguins_df, aes(x = body_mass_g)) +
  geom_density(fill = "orange", alpha = 0.3)
#> Warning: Removed 2 rows containing non-finite values (stat_density).

Your intuition is correct, we can overlap multiple density plots. For this we will use the fill aesthetic and create a different fill for each penguin species. To have some visibility, we set the alpha level to a more transparent value.

ggplot(penguins_df, aes(x = body_mass_g, fill = species)) +
  geom_density(alpha = 0.35, color = "white")
#> Warning: Removed 2 rows containing non-finite values (stat_density).

Ridgeline

This one is quite spectacular looking and informative. It has a similar function as the overlayed histograms but presents a much clearer data. For this, we need the ggridges package which is a ggplot2 extension. They are most useful when we have to visualize distributions over times (or a large amount of distributions). For this we will use the airquality dataset, which is a built in example dataset.

airquality_df <- airquality %>% 
  mutate(
    temp_c = (Temp - 30) / 2,
    month = factor(Month, ordered = TRUE)
    )
  

head(airquality_df)
#>   Ozone Solar.R Wind Temp Month Day temp_c month
#> 1    41     190  7.4   67     5   1   18.5     5
#> 2    36     118  8.0   72     5   2   21.0     5
#> 3    12     149 12.6   74     5   3   22.0     5
#> 4    18     313 11.5   62     5   4   16.0     5
#> 5    NA      NA 14.3   56     5   5   13.0     5
#> 6    28      NA 14.9   66     5   6   18.0     5
ggplot(airquality_df, aes(x = temp_c, y = month, fill = month)) +
  geom_density_ridges(scale = 0.8, alpha = 0.5)

My all time favorite example for a ridge plot is probably the fun survey on Perceptions of probability.

2.3.0.1 Jitter plot

Plotting the actial data points also provide a great visual overview of the data distribution. However, in order to avoid overplotting the points we will jitter them, using the appropriately names geom_jitter() function.

ggplot(penguins_df, aes(x = species, y = body_mass_g)) +
  geom_jitter()
#> Warning: Removed 2 rows containing missing values (geom_point).

Box plot

The box plot is a good way to visualize distributions in your data, although it can be a but misleading. Interpretation of the box plot is that the following. The box contains 50% of the values, the whiskers are the minimum and maximum values without the outlines, the line inside the box is the median. The upper and lower edges of the box are the first and third quartiles, respectively.

In visual form:

Source: Wickham, Hadley, and Garrett Grolemund. R for data science: import, tidy, transform, visualize, and model data. " O’Reilly Media, Inc.", 2016.

ggplot(penguins_df, aes(x = species, y = body_mass_g)) +
  geom_boxplot()
#> Warning: Removed 2 rows containing non-finite values (stat_boxplot).

To add some visual flair, we can color the outlier points and also show the actual datapoints by also plotting geom_jitter.


ggplot(penguins_df, aes(x = species, y = body_mass_g)) +
  geom_boxplot(outlier.color = "orange") + 
  geom_jitter(alpha = 0.25)
#> Warning: Removed 2 rows containing non-finite values (stat_boxplot).
#> Warning: Removed 2 rows containing missing values (geom_point).

Violin plots

Violin plots are the density based variants of the boxplots. They are created by plotting a density plot for the variable, rotating it 90 degrees and then mirroring the plot.

ggplot(penguins_df, aes(x = species, y = body_mass_g)) +
  geom_violin()
#> Warning: Removed 2 rows containing non-finite values (stat_ydensity).

Sina plots

Sina plots are essentially representing data points with the same density as the violin plots. The geom_sina function is provded by the ggforce ggplot extension.

ggplot(penguins_df, aes(x = species, y = body_mass_g)) +
  geom_sina()
#> Warning: Removed 2 rows containing non-finite values (stat_sina).

Similarly to the overlayed boxplot we can combine the sina plot with the violin plot to some nice effect. (I used a pre-defined theme here, more on this shortly!)

ggplot(penguins_df, aes(x = species, y = body_mass_g)) +
  geom_sina() + 
  geom_violin(alpha = 0.25, fill = "grey") +
  theme_minimal()
#> Warning: Removed 2 rows containing non-finite values (stat_sina).
#> Warning: Removed 2 rows containing non-finite values (stat_ydensity).

2.4 x ~ y relationships

So far we were mainly visualizing descriptive statistical properties, now it is time to check for some association within our datasets.

Source: Fundamentals of Data Visualization, 5.1 ch

Scatterplot

We use scatter plot to illustrate some association between two continuous variable. Usually, the y axis is our dependent variable (the variable which is explained) and x is the independent variable, which we suspect that drives the association.

Now, we want to know what is the association between the GDP per capita and life expectancy.

ggplot(data = gapminder_df,
       mapping = aes(x = lifeExp,
                     y = gdpPercap)) +
  geom_point()

What are the problems with this plot?

Now that we have a basic figure, let’s make it better and deal with some of the issues. We transform the y axis values with the scale_y_log10() and add text to our plot with the labs() function. Within geom_point() we can also specify geom specific options, such as the alpha level (transparency). This way we address the issue of overplotting as well as the log transformation creates a “more normal” distribution.

ggplot(data = gapminder_df,
       mapping = aes(x = lifeExp,
                     y = gdpPercap)) +
    geom_point(alpha = 0.25) +
    scale_y_log10() +
    labs(
      y = "GDP per capita", 
      x = "Life expectancy",
      title = "Connection between GDP and Life expectancy",
      subtitle = "Points are country-years",
      caption = "Source: Gapminder"
      )

To add some analytical power to our plot we can use geom_smooth() and choose a method for it’s smoothing function. It can be lm, glm, gam, loess, and rlm. We will use the linear model (“lm”). Note: this is purely for illustrative purposes, as our data points are country-years, so “lm” is not a proper way to fit a regression line to this data. This example also shows how to plot two geoms into one figure.

ggplot(data = gapminder_df,
       mapping = aes(x = lifeExp,
                     y = gdpPercap)) +
    geom_point(alpha = 0.25) + 
    geom_smooth(method = "lm", se = TRUE, color = "orange") +
    scale_y_log10() +
    labs(
      y = "GDP per capita", 
      x = "Life expectancy",
      title = "Connection between GDP and Life expectancy",
      subtitle = "Points are country-years",
      caption = "Source: Gapminder"
    )

what if we want to see how each continent fares in this relationship? We need to include a new argument in the mapping function: color =. Now it is clear that European countries (country-years) are clustered in the high-GDP/high life longevity upper right corner.

ggplot(data = gapminder_df,
       mapping = aes(x = lifeExp,
                     y = gdpPercap,
                     color = continent)) +
    geom_point(alpha = 0.5) + 
    geom_smooth(method = "lm", se = TRUE, color = "black") +
    scale_y_log10() +
    labs(
      y = "GDP per capita", 
      x = "Life expectancy",
      title = "Connection between GDP and Life expectancy",
      subtitle = "Points are country-years",
      caption = "Source: Gapminder"
    )

We add horizontal line or vertical line to our plot, if we have a particular cutoff that we want to show. We can add these with the geom_hline() and geom_vline() functions. In the below example we’ll draw horizontal and vertical lines at the median and average of the x and y axis. Similarly to the geoms introduced so far, we can change the type of these lines, with the linetype option. It can be “blank”, “solid”, “dashed”, “dotted”, “dotdash”, “longdash”, “twodash”.

ggplot(data = gapminder_df,
       mapping = aes(x = lifeExp,
                     y = gdpPercap,
                     color = continent)) +
  geom_point(alpha = 0.5) + 
  geom_vline(xintercept = mean(gapminder_df$lifeExp), linetype = "dashed") +
  geom_hline(yintercept = median(gapminder_df$gdpPercap), linetype = "dotted") +
  scale_y_log10() +
  labs(
    y = "GDP per capita", 
    x = "Life expectancy",
    title = "Connection between GDP and Life expectancy",
    subtitle = "Points are country-years",
    caption = "Source: Gapminder"
  )

Bubble chart

To replicate the famous gapminder bubble chart we will encode a third dimension alongside our life expectancy and GDP percapita variables. This is going to be the size of the country. In order to have a manageable plot we will use the 2002 cross section. Using size we can embed a new dimension, however we should be mindful not to overburden the audience with information.

gapminder_df %>% 
  filter(year == 2002) %>% 
  ggplot(aes(x = lifeExp, y = gdpPercap, size = pop, color = continent)) +
  geom_point(alpha = 0.8) +
  scale_y_log10()

Line graphs

For this we use data on stock closing prices. As we are now familiar with the ggplot2 syntax, I do not write out all the data = and mapping =. Note that here we have to use the group argument to group our data according to companies.

ggplot(stocks, aes(date, stock_index, group = company)) +
  geom_line()

Add some refinements. One way to highlight our focus (let’s say $AMZN stocks) is to use the gghighlight package which provides this useful extension.

ggplot(stocks, aes(date, stock_index, group = company)) +
  geom_line() +
  gghighlight(
    company == "AMZN.Close", 
    use_direct_label = TRUE, 
    label_params = list(size = 3)
    ) +
  labs(
    x = "", 
    y = "Index, 2006/01/11 = 100",
    title = "Closing daily prices for selected tech stocks",
    subtitle = "Data from 2016-01-10 to 2018-01-10",
    caption = "source: Yahoo Finance"
    )
#> Warning: Tried to calculate with group_by(), but the calculation failed.
#> Falling back to ungrouped filter operation...

Faceting (or creating small multiples) is an excellent way to declutter our plot. To drop the legend from the side, we’ll drop the guide from the scale_color_discrete function using the guide = FALSE option. To highlight the trend in our stock data, we can color the area under the curves as well, with the geom_area.

ggplot(stocks, aes(date, stock_index, color = company)) +
  geom_line() +
  geom_area(aes(fill = company), alpha = 0.6) +
  scale_color_discrete(guide = FALSE) +
  scale_fill_discrete(guide = FALSE) +
  labs(
    x = "", 
    y = "Index, 2006/01/11 = 100",
    title = "Closing daily prices for selected tech stocks",
    subtitle = "Data from 2016-01-10 to 2018-01-10",
    caption = "source: Yahoo Finance"
    ) +
    facet_wrap(~company, nrow = 4)

QUICK EXCERCISE: Create a plot from the gapminder datase, where: you show the distribution of life expectancy for each continent. Use facet_wrap or facet_grid

3 Themes and plot elements

3.1 Themes

In this section we will go over some of the elements that you can modify in order to get an informative and nice looking figure. ggplot2 comes with a number of themes. You can play around the themes that come with ggplot2 and you can also take a look at the ggthemes package, where I included the economist theme. Another notable theme collection is the hrbthemes package. The BBC also published their R package which they use to create their graphics. You can find it on GitHub here: https://github.com/bbc/bbplot

Try out a couple to see what they differ in! The ggthemes package has a nice collection of themes to use. The theme presets can be used with the theme_*() function.

One of my personal favorite is the theme_minimal()

ggplot(gapminder_df, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.25) + 
  scale_x_log10() + 
  theme_minimal() # adding our chosen theme

3.2 Plot elements

Of course we can set all elements to suit our need, without using someone else’s theme.

The key plot elements that we will look at are:

  • labels
  • grid lines
  • fonts
  • colors
  • legend
  • axis breaks

Adding labels, title, as we did before. For this example we will use the same plot that we created at the beggining of the session.

ggplot(penguins_df, aes(x = body_mass_g, y = flipper_length_mm, color = species)) +
  geom_point() +
  labs(
    title = "Penguins, their mass and their flippers",
    subtitle = "The positive relationship between body mass and flipper size",
    caption = "Data: Gorman, Williams & Fraser (2014)",
    x = "Body mass (g)",
    y = "Flipper length (mm)",
    color = ""
    )
#> Warning: Removed 2 rows containing missing values (geom_point).

Let’s use a different color scale! We can use a color brewer scale (widely used for data visualization). To check the various palettes, see http://colorbrewer2.org

ggplot(penguins_df, aes(x = body_mass_g, y = flipper_length_mm, color = species)) +
  geom_point() +
  scale_color_brewer(name = "Species", palette = "Set2") # adding the color brewer color scale
#> Warning: Removed 2 rows containing missing values (geom_point).

Or we can define our own colors:


ggplot(penguins_df, aes(x = body_mass_g, y = flipper_length_mm, color = species)) +
  geom_point() +
  scale_color_manual(values=c("coral3", "deepskyblue3", "orange")) # adding our manual color scale
#> Warning: Removed 2 rows containing missing values (geom_point).

To make each species more distinc we can use different shapes as well.


ggplot(penguins_df, aes(x = body_mass_g, y = flipper_length_mm, color = species, shape = species)) +
  geom_point()
#> Warning: Removed 2 rows containing missing values (geom_point).

To give a brief glimpse how customizable a ggplot2 figure is, we are going to modify some theme elements and change the look of our plot drastically to the better.

To clean up clutter, we will remove the background, and only leave some of the grid behind: - We can hide the tickmarks with modifying the theme() function, and setting the axis.ticks to element_blank(). - Hiding gridlines also requires some digging in the theme() function with the panel.grid.minor or .major functions. - If you want to remove a gridline on a certain axis, you can specify panel.grid.major.x. We can also set the background to nothing. - We can modify the gridline within the element_line() function. - As a final touch, the axis line is also recolored

ggplot(penguins_df, aes(x = body_mass_g, y = flipper_length_mm, color = species, shape = species)) +
  geom_point() +
  theme(
    axis.ticks = element_blank(), # removing axis ticks
    panel.grid.major.y = element_line(color = "grey", size = 0.1, linetype = "dotted"), # recoloring the panel grids
    panel.background = element_blank(), # removing the background
    axis.line = element_line(colour = "grey")
    ) 
#> Warning: Removed 2 rows containing missing values (geom_point).

Finally, let’s move the legend around. Or just remove it with theme(legend.position="none"). We also do not need the background of the legend, so remove it with legend.key, and play around with the text elements of the plot with text.


ggplot(penguins_df, aes(x = body_mass_g, y = flipper_length_mm, color = species, shape = species)) +
  geom_point() +
  theme(
    axis.ticks = element_blank(), # removing axis ticks
    panel.grid.major.y = element_line(color = "grey", size = 0.1, linetype = "dotted"), # recoloring the panel grids
    panel.background = element_blank(),
    axis.line = element_line(colour = "grey"),
    legend.title = element_text(size = 12), # setting the legends text size
    text = element_text(face = "plain", family = "sans"), # setting global text options for our plot
    legend.key=element_blank(),
    legend.position = "bottom"
    )
#> Warning: Removed 2 rows containing missing values (geom_point).

In the final version, we add a custom color palette, the labels and all the modification we made so far.

ggplot(penguins_df, aes(x = body_mass_g, y = flipper_length_mm, color = species, shape = species)) +
  geom_point() +
  labs(
    title = "Penguins, their mass and their flippers",
    subtitle = "The positive relationship between body mass and flipper size",
    caption = "Data: Gorman, Williams & Fraser (2014)",
    x = "Body mass (g)",
    y = "Flipper length (mm)",
    color = ""
    ) +
  scale_color_brewer(name = "Species", palette = "Set2") +
  scale_shape(guide = FALSE) +
  theme(
    axis.ticks = element_blank(), # removing axis ticks
    panel.grid.major.y = element_line(color = "grey", size = 0.1, linetype = "dotted"), # recoloring the panel grids
    panel.background = element_blank(),
    axis.line = element_line(colour = "grey"),
    legend.title = element_text(size = 12), # setting the legends text size
    text = element_text(face = "plain", family = "sans"), # setting global text options for our plot
    legend.key=element_blank(),
    legend.position = "bottom"
    )
#> Warning: Removed 2 rows containing missing values (geom_point).

While we are at it, we want to have labels for our data. For this, we’ll create a plot which can exploit this. Let’s return to the gapminder data for this exercise. To have a manageable sized number of observations we are going to filter the data somewhat.

What we use is the geom_text to have out labels in the chart.

gapminder_small <- gapminder_df %>% 
  filter(
    lifeExp >= 72.5, 
    gdpPercap >= 10000, 
    continent == "Europe", 
    year == 2002
    )

ggplot(gapminder_small, aes(lifeExp, gdpPercap, label = country)) + # we add the labels!
  geom_point() +
  geom_text()

To avoid overlapping text, use the ggrepel package which provides this functionality via the ggrepel::geom_text_repel and the ggrepel::geom_label_repel functions.

ggplot(gapminder_small, aes(lifeExp, gdpPercap, label = country)) + 
  geom_point() +
  ggrepel::geom_text_repel()

notice the different outcome of geom_label instead of geom_text.

ggplot(gapminder_small, aes(lifeExp, gdpPercap, label = country)) + # we add the labels!
  geom_point() +
  geom_label() # and use the geom label

If we want to label a specific set of countries we can do it from inside ggplot, without needing to touch our data.

ggplot(gapminder_df, aes(gdpPercap, lifeExp, label = country)) + # we add the labels!
  geom_point(alpha = 0.25) +
  geom_text(aes(label = if_else(gdpPercap > 60000, country, NULL))) # we add a conditional within the geom. Note the nudge_x!
#> Warning: Removed 1699 rows containing missing values (geom_text).

QUICK EXCERCISE! With the tools we’ve covered, make a sensible version of this chart. The details are up to you!

ggplot(data = midwest,
       aes(x = popdensity, y = popblack, color = state)) + 
  geom_point(aes(shape = state)) + 
  geom_abline(color = "yellow")

  1. This short section builds heavily on the Part II. of Claus Wilke’s brilliant Fundamentals of Data Visualization↩︎

  2. In data journalism, the Financial Times, BBC, fivethirtyeight.com are the most famous ggplot2 users. In the industry, airbnb, uber are also prominent users.↩︎